Estimating channel parameters in multi-input, multi-output (MIMO) systems

ABSTRACT

In a communication system, and in particular a wireless Orthogonal Frequency Division Multiplexing (OFDM) communication system, the present invention provides systems for estimating parameters of a channel across which a signal is transmitted. The present invention may be used in a Multi-Input, Multi-Output (MIMO) system in which the data is transmitted from any number of transmitting antennas and received by any number of receiving antennas. The number of transmitting and receiving antennas does not necessarily have to be the same. Circuitry is provided for calculating parameters indicative of the characteristics of the communication channel. Estimates of channel parameters may include channel estimates and noise variance estimates.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to copending U.S. provisionalapplication entitled “Parameter Estimation for MIMO OFDM Systems,”having Ser. No. 60/286,130, filed on Apr. 24, 2001, which is entirelyincorporated herein by reference.

[0002] This application is related to copending U.S. provisionalapplication entitled, “Synchronization for MIMO OFDM Systems,” havingSer. No. 60/286,180, filed Apr. 24, 2001, which is entirely incorporatedherein by reference.

TECHNICAL FIELD OF THE INVENTION

[0003] The present invention is generally related to wirelesscommunication systems that employ Orthogonal Frequency DivisionMultiplexing (OFDM). More particularly, the present invention is relatedto an apparatus and method for estimating channel parameters in aMulti-Input, Multi-Output (MIMO) OFDM system.

BACKGROUND OF THE INVENTION

[0004] In wireless communication systems, recent developments have beenmade using technologies wherein multiple signals are simultaneouslytransmitted over a single transmission path. In Frequency DivisionMultiplexing (FDM), the frequency spectrum is divided into sub-channels.Information (e.g. voice, video, audio, text, etc.) is modulated andtransmitted over these sub-channels at different sub-carrierfrequencies.

[0005] In Orthogonal Frequency Division Multiplexing (OFDM) schemes, thesub-carrier frequencies are spaced apart by precise frequencydifferences. Because of the ability of OFDM systems to overcome themultiple path effects of the channel, and to transmit and receive largeamounts of information, much research has been performed to advance thistechnology. By using multiple transmitting antennas and multiplereceiving antennas in OFDM systems, it is possible to increase thecapacity of transmitted and received data while generally using the sameamount of bandwidth as in a system with one transmit and one receiveantenna.

[0006] OFDM technologies are typically divided into two categories. Thefirst category is the Single-Input, Single-Output (SISO) scheme, whichutilizes a single transmitting antenna to transmit radio frequency (RF)signals and a single receiving antenna to receive the RF signals. Thesecond category is the Multi-Input, Multi-Output (MIMO) scheme, whichuses multiple transmitting antennas and multiple receiving antennas.

[0007] In typical communication systems, training symbols, or preamble,at the beginning of data frames, are usually added as a prefix to thedata symbols. The data symbols, of course, include the useful data orinformation (e.g., voice, data, video, etc.), which is meant to betransmitted to a remote location. The training symbols in SISO systemsare used to provide synchronization of the received signals with respectto the transmitted signals, as well as to provide channel parameterestimation.

[0008] Although training symbols used for SISO systems can be used toprovide synchronization in a MIMO system, the training symbols cannotprovide for channel parameter estimation in the MIMO system. In fact, nomethod or apparatus exists for MIMO systems that is capable of providingtime and frequency synchronization as well as channel parameterestimation. Thus, a need exists for a method and apparatus that iscapable of providing time and frequency synchronization in MIMO systemsand can further perform channel estimation.

SUMMARY OF THE INVENTION

[0009] The present invention provides systems and methods that overcomethe deficiencies of the prior art as mentioned above. The presentinvention utilizes a sequence of training symbols or preambles that maybe used in both Single-Input, Single-Output (SISO) and Multi-Input,Multi-Output (MIMO) systems, using any number of transmitting andreceiving antennas. An embodiment of the present invention comprises anapparatus that can be used to estimate channel parameters across which adata frame is transmitted in a MIMO system. In conjunction with asynchronization scheme, the parameter estimator calculates an accurateestimation of the characteristics of the channel, thereby making theMIMO systems operational. The present invention achieves an accurateestimation of channel parameters and further achieves synchronization inthe time domain and frequency domain and, therefore, enables MIMOsystems to operate acceptably.

[0010] One MIMO Orthogonal Frequency Division Multiplexing (OFDM) systemof the present invention includes a number of OFDM modulators, whichprovide data frames to be transmitted across a channel. The data framesof the present invention comprise one or more training symbols, aplurality of data symbols, and cyclic prefixes inserted between the datasymbols. A number of transmitting antennas corresponding to the numberof modulators are used to transmit the modulated signals over thechannel. A number of receiving antennas are used to receive thetransmitted signals. The received signals are demodulated by a number ofOFDM demodulators corresponding to the number of receiving antennas anddecoded by an OFDM decoder, which processes the data frames. Byutilizing the structure embedded in the training symbols, the MIMOsystem of the present invention is capable of providing time andfrequency synchronization as well as perform channel estimation.

[0011] A method of the present invention is also provided, whereinparameter estimation is carried out in a MIMO system. The methodincludes producing data frames comprising at least one training symbol,multiple data symbols and cyclic prefixes. The data frames aretransmitted over the channel, received, demodulated, and processed. Byprocessing the training symbol of the data frame, an estimation of theparameters of the channel may be achieved from the data frame.

[0012] Other systems, methods, features, and advantages of the presentinvention will become apparent to a person having skill in the art uponexamination of the following drawings and detailed description. All suchadditional systems, methods, features, and advantages are within thescope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Many aspects of the invention can be better understood withreference to the following drawings. Moreover, in the drawings, likereference numerals designate corresponding parts throughout the severalviews.

[0014]FIG. 1 is a block diagram illustrating an example embodiment of aMulti-Input, Multi-Output (MIMO) Orthogonal Frequency DivisionMultiplexing (OFDM) system.

[0015]FIG. 2 is a block diagram illustrating an example embodiment ofthe MIMO encoder shown in FIG. 1.

[0016]FIG. 3 is a block diagram illustrating an example embodiment ofone of the OFDM modulators shown in FIG. 1.

[0017]FIG. 4 illustrates an example frame structure for a MIMO OFDMsystem.

[0018]FIG. 5 is a block diagram illustrating an example matrix of atransmitted sequence structure and an example matrix of a receivedsequence structure using the modulator/demodulator arrangement shown inFIG. 1.

[0019]FIG. 6 illustrates a three-dimensional representation of thereceived sequence structure in detail.

[0020]FIG. 7 is a block diagram illustrating an example embodiment ofone of the OFDM demodulators shown in FIG. 1.

[0021]FIG. 8 is a block diagram illustrating an example embodiment ofthe decoder shown in FIG. 1.

[0022]FIG. 9 is a flow chart of a first embodiment of a method forcalculating fine channel estimates.

[0023]FIG. 10 is a flow chart of a second embodiment of a method forcalculating fine channel estimates.

[0024]FIG. 11 is a flow chart of an example embodiment of a method forcalculating noise variance estimates when the signal transmissionmatrices are unitary.

[0025]FIG. 12 is a flow chart of an example embodiment of a method forcalculating noise variance estimates when the signal transmissionmatrices are not unitary.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0026] In FIG. 1, an example embodiment of a Multi-Input, Multi-Output(MIMO) Orthogonal Frequency Division Multiplexing (OFDM) communicationsystem 6 of the present invention is shown. The communication system 6in this example embodiment may be implemented as a wireless system forthe transmission and reception of data across a wireless channel 19. Thecommunication system 6, for example, may be part of a wireless LocalArea Network (LAN) system or wireless Metropolitan Area Network (MAN)system, cellular telephone system, or other type of radio or microwavefrequency system incorporating either one-way or two-way communicationover a range of distances. The communication system 6 may transmit in arange from 2 to 11 GHz, for example, such as in the unlicensed 5.8 GHzband using a bandwidth of about 3-6 MHz.

[0027] It is also possible for the present invention to be used in asystem that comprises an array of sub-channel communication links thatcarry a number of signals transmitted by a number of transmittingelements to each of a number of receiving elements. In this latter case,communication links, such as wires in a wiring harness or somealternative wired transmission system, for example, could be used overthe distance between a data source and a receiver.

[0028] In the example embodiment of FIG. 1, a transmitter 8 transmitssignals across the wireless channel 19 and a receiver 10 receives thetransmitted signals. The transmitter 8 comprises a data source 12, whichprovides the original binary data to be transmitted from the transmitter8. The data source 12 may provide any type of data, such as, forexample, voice, video, audio, text, etc. The data source 12 applies thedata to an encoder 14, which encodes the data to allow for errorcorrection. The encoder 14 further processes the data so that certaincriterion for space-time processing and OFDM are satisfied. The encoder14 separates the data onto multiple paths in the transmitter 8, each ofwhich will hereinafter be referred to as a transmit diversity branch(TDB). The separate TDBs are input into OFDM modulators 16, each ofwhich modulates the signal on the respective TDB for transmission by thetransmitting antennas 18. The present invention may be used in aSingle-Input, Single-Output (SISO) system, which may be considered as aspecial case of MIMO wherein the number of transmitting and receivingantennas is one. In the SISO system example, separation of the data bythe encoder 14 is not necessary since only one OFDM modulator 16 andantenna 18 is used.

[0029] During the encoding by the encoder 14 and modulating by the OFDMmodulators 16, data is normally bundled into groups such that thecollection of each group of data is referred to as a “frame.” Details ofthe frame as used in the present invention will be described in moredetail below with reference to FIG. 4. Each frame along each TDB isoutput from a respective OFDM modulator 16. As illustrated in FIG. 1,any number of OFDM modulators 16 may be used. The number of OFDMmodulators 16 and respective transmitting antennas 18 may be representedby a variable “Q.” The OFDM modulators 16 modulate the respective framesat specific sub-carrier frequencies and respective transmitting antennas18 transmit the modulated frames over the channel 19.

[0030] On the side of the receiver 10, a number “L” of receivingantennas 20 receives the transmitted signals, which are demodulated by anumber L of respective OFDM demodulators 22. The number L may representany number and is not necessarily the same as the number Q. In otherwords, the number Q of transmitting antennas 18 may be different fromthe number L of receiving antennas 20, or they may alternatively be thesame. The outputs of the demodulators 22 are input into a decoder 24,which combines and decodes the demodulated signals. The decoder 24outputs the original data, which may be received by a device (not shown)that uses the data.

[0031] The communication system 6 may comprise one or more processors,configured as hardware devices for executing software, particularlysoftware stored in computer-readable memory. The processor can be anycustom made or commercially available processor, a central processingunit (CPU), an auxiliary processor among several processors associatedwith a computer, a semiconductor based microprocessor (in the form of amicrochip or chip set), a macroprocessor, or generally any device forexecuting software instructions. Examples of suitable commerciallyavailable microprocessors are as follows: a PA-RISC seriesmicroprocessor from Hewlett-Packard Company, an 80x86 or Pentium seriesmicroprocessor from Intel Corporation, a PowerPC microprocessor fromIBM, a Sparc microprocessor from Sun Microsystems, Inc, a 68xxx seriesmicroprocessor from Motorola Corporation, or a 67xxx series DigitalSignal Processor from the Texas Instruments Corporation.

[0032] When the communication system 6 is implemented in software, itshould be noted that the communication system 6 can be stored on anycomputer-readable medium for use by or in connection with anycomputer-related system or method. In the context of this document, acomputer-readable medium is an electronic, magnetic, optical, or otherphysical device or means that can contain or store a computer programfor use by or in connection with a computer related system or method.The communication system 6 can be embodied in any computer-readablemedium for use by or in connection with an instruction execution system,apparatus, or device, such as a computer-based system,processor-containing system, or other system that can fetch theinstructions from the instruction execution system, apparatus, or deviceand execute the instructions. In the context of this document, a“computer-readable medium” can be any means that can store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device. Thecomputer-readable medium can be, for example but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, device, or propagation medium. Examplesof the computer-readable medium include the following: an electricalconnection having one or more wires, a portable computer diskette, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM, EEPROM, or Flash memory), anoptical fiber, and a portable compact disc read-only memory (CDROM).Note that the computer-readable medium could even be paper or anothersuitable medium upon which the program is printed, as the program can beelectronically captured, via for instance optical scanning of the paperor other medium, then compiled, interpreted or otherwise processed in asuitable manner if necessary, and then stored in a computer memory.

[0033] In an alternative embodiment, where the communication system 6 isimplemented in hardware, the communication system can be implementedwith any or a combination of the following technologies, which are eachwell known in the art: one or more discrete logic circuits having logicgates for implementing logic functions upon data signals, an applicationspecific integrated circuit (ASIC) having an appropriate combination oflogic gates, a programmable gate array (PGA), a field programmable gatearray (FPGA), etc.

[0034] The encoder 14 and OFDM modulators 16 of the transmitter 8 willnow be described with respect to FIGS. 2 and 3. FIG. 2 shows details ofan example embodiment of the encoder 14 shown in FIG. 1. The encoder 14may be configured such that data from the data source 12 is encoded by achannel encoder 26, which adds parity to the original data to producechannel encoded data. The channel encoder 26 encodes the data using ascheme that is recognized by the decoder 24 of the receiver 10 andenables the decoder 24 to detect errors in the received data. Errors mayarise as a result of environmental conditions of the channel 19 or noiseinadvertently added by the transmitter 8 or receiver 10.

[0035] The encoder 14 further includes a symbol mapper 28, which mapsthe channel-encoded data into data symbols. The symbol mapper 28 groupsa predetermined number of bits such that each group of bits constitutesa specific symbol chosen from a pre-determined alphabet. The symbolmapper 28 further lays out a stream of data symbols within the structureof a frame.

[0036] The encoder 14 further includes a space-time processor 30 thatprocesses the data symbol stream received from the symbol mapper 28 andoutputs the processed data symbols via the respective TDBs. Thespace-time processor 30 encodes the data symbol stream in a manner suchthat the receiver 10 is capable of decoding the signals. The datasymbols in the TDBs are distributed over Q lines that will eventually betransmitted at precise frequencies spaced apart from each other by apredetermined difference in frequency. By providing a specific frequencydifference between the multiple sub-channels, orthogonality can bemaintained, thereby preventing the OFDM demodulators 22 from picking upfrequencies other than their own designated frequency.

[0037] Each TDB provides an input to a respective adder 34. The otherinput into each of the adders 34 is connected to the output of apilot/training symbol inserter 32, which provides pilot symbols andtraining symbols to be inserted into the frames on the TDBs. Symbolsinserted periodically within the data symbols will be referred to hereinas “pilot symbols.” These periodic pilot symbols may be insertedanywhere in the stream of the data symbols. If a continuous burst ofsymbols is inserted by the pilot/training symbol inserter 32, this typeof symbol will be referred to herein as “training symbols” whichconstitute the preamble. The training symbols preferably are inserted atthe beginning of the frame. However, the training symbols may beinserted onto the frame in a location other than at the beginning of theframe, such as at the end or in the middle of the frame.

[0038] The pilot/training symbol inserter 32 may be configured so thatit is capable of storing multiple sets of training symbols or pilotsymbols. In this case, a particular set may be selected, for example,based on desirable communication criteria established by a user. Thetraining symbols for each respective sub-channel may preferably beunique to the particular sub-channel. In order to accommodate amplitudedifferences between the sub-channels, the training symbols may bedesigned and adjusted to maintain a constant amplitude at the output ofeach sub-channel.

[0039] Training symbols are preferably transmitted once for every frame.Training symbols are used for periodic calibration (synchronization andchannel parameter estimation) whereas pilot symbols are used for minoradjustments to deal with the time-varying nature of the channel. Thetraining symbols may be indicative of calibration values or known datavalues. These calibration values or known values may be transmittedacross the channel, and used to calibrate the communication system 6.Any necessary refinements may be made to the communication system 6 ifthe received calibration values do not meet desirable specifications.

[0040] Furthermore, the training symbols may be used as specific typesof calibration values for calibrating particular channel parameters. Byinitially estimating these channel parameters, offsets in the timedomain and frequency domain may be accounted for so as to calibrate thecommunication system 6. The training sequence may or may not bypass anInverse Discrete Fourier Transform (IDFT) stage 38, which is a part ofthe embodiment of the OFDM modulator 16 of FIG. 3. A training sequencethat bypasses the IDFT stage 38 and is directly input into a digital toanalog converter (DAC) 44 is referred to herein as a directlymodulatable training sequence. Examples of such training sequences maybe “chirp-like” sequences. These sequences cover each portion of thebandwidth used by the communication system 6. Hence, channel responsecan be easily determined. In general, a chirp sequence in the timedomain is given by the equation:${s_{n} = {{\cos \left( \frac{\pi \quad n^{2}}{N} \right)} + {j\quad {\sin \left( \frac{\pi \quad n^{2}}{N} \right)}}}},{n = 0},1,\ldots \quad,{N - 1},$

[0041] where j is given by {square root}{square root over (−1)} and isused to denote the quadrature component of the signal. It should benoted that the term s_(n) refers to a time domain signal on the side ofthe transmitter 8. Frequency domain signals on the transmitter side willhereinafter be referenced by capital letters S_(k). Time and frequencydomain signals on the receiver side will hereinafter be written as r_(n)and R_(k), respectively. Other modifications of the chirp-like sequencemay be Frank-Zadoff sequences, Chu sequences, Milewski sequences,Suehiro polyphase sequences, and sequences given by Ng et al. Byobserving the response of the receiver 10 to the chirp signals, thechannel parameters may be estimated.

[0042] In the case when the IDFT stage 38 is not bypassed, a trainingsequence may be generated by modulating each of the symbols on the TDBswith a known sequence of symbols in the frequency domain and passing thesymbols through the IDFT stage 38. Generally, such a known sequence ofsymbols is obtained from an alphabet which has its constituents on theunit circle in the complex domain and such that the resultant sequencein the time domain has a suitable Peak to Average Power Ratio (PAPR).The term “alphabet” in communication systems is defined as a finite setof complex values that each of the symbols can assume. For example, analphabet of a binary phase shift keying (BPSK) system consists of values+1 and −1 only. An alphabet for a quaternary phase shift keying (QPSK)system consists of the values 1+j, −1+j, 1−j, and −1−j. For example, thetraining sequence may be generated by modulating each of the tones ofthe OFDM block using a BPSK alphabet, which consists of symbols +1 and−1. The synchronization scheme may be very general such that any knownsequence having suitable properties, such as low PAPR, may be used toform the training sequence.

[0043] With reference again to FIG. 2, the adders 34 add the trainingsymbols and pilot symbols to the frame. Other embodiments may be used inplace of the adders 34 for combining the training symbols and pilotsymbols with the data symbols in the frame. Furthermore, the adders 34may include additional inputs to allow for flexibility when adding thepilot/training symbols or in the combining of multiple training symbolsor even selectable training symbols. After the training symbols areinserted into frames on the respective TDBs, the frames are output fromthe encoder 14 and input in respective OFDM modulators 16.

[0044]FIG. 3 shows an example embodiment of an OFDM modulator 16, whichreceives signals along one of the TDBs. The number of OFDM modulators 16is preferably equal to the number of transmitting antennas 18. In SISOsystems, there is only one OFDM modulator 16 and one transmittingantenna 18. In MIMO systems, there may be any number of OFDM modulators16 and transmitting antennas 18.

[0045] The respective signal from the encoder 14 is input into aserial-to-parallel converter 36 of the OFDM modulator 16. Theserial-to-parallel converter 36 takes N symbols received in a serialformat and converts them into a parallel format. The variable N will bereferred to herein as the blocksize of the OFDM symbol. The N parallelsymbols are processed by an Inverse Discrete Fourier Transform (IDFT)stage 38, which transforms the frequency signals to the time domain. TheN number of transformed symbols in the time domain will be referred toherein as samples.

[0046] A method is proposed herein to design the training symbols suchthat the transforms of all the sequences from the IDFT stage 38 willhave a constant magnitude. By maintaining a constant magnitude at theoutput of each of the IDFT stages 38 within their respective modulators,one of the main problems of OFDM, i.e., peak to average power ratio(PAPR), is solved. The receiver 10 can thus more accurately estimate thechannel parameters, which are used by the receiver 10 to synchronize thereceived signals in the time and frequency domains, as will be describedbelow in more detail.

[0047] The output from the IDFT stage 38 is input into a cyclic prefixinserter 40, which inserts an additional number of samples for every Nsamples. The number of samples inserted by the cyclic prefix inserter 40will be referred to herein by the variable “G.” The G samples areintended to be inserted as guard intervals to separate the N adjacentdata symbols from each other in time by a separation adequate tosubstantially eliminate Inter Symbol Interference (ISI). The cyclicprefix inserter 40 repeats G samples from a latter portion of the Nsamples output from the IDFT stage 38 and inserts the G samples as aprefix to each of the data samples. Preferably, the time length of thecyclic prefix is greater than the maximum time delay of a transmittedsignal across the channel 19. Since the nature of the channel 19 may besusceptible to a variation in the delay time from the transmittedantennas 18 to the receiving antennas 20, it may be desirable toincrease, or even double, the length of cyclic prefixes of the preambleto ensure that the time delay of the channel does not exceed the time ofthe cyclic prefix, thereby eliminating ISI.

[0048] The G+N samples, herein referred to as an OFDM symbol, are thenconverted from a parallel format to a serial format usingparallel-to-serial converter 42, and then inputted to adigital-to-analog converter (DAC) 44 for conversion into analog signals.The output from the DAC 44 is input into a mixer 48. A local oscillator46 provides a signal having the carrier frequency to the other input ofthe mixer 48 to up-convert the respective OFDM symbol from baseband toRF.

[0049] After the respective frame has been mixed with a carrierfrequency that is set by the respective local oscillator 46, the frameis amplified by an amplifier 50. As indicated above, one of thedrawbacks to any OFDM signal is that it generally has a high PAPR. Toaccommodate this drawback, the amplifier 50 may be backed off to preventit from going into its non-linear region. However, the present inventionmay provide certain specific sequences that can be used in order to makethe PAPR minimal or unity.

[0050] Each OFDM modulator 16 preferably comprises the same componentsas the OFDM modulator 16 shown in FIG. 3. Other techniques for designingthe OFDM modulators 16 may be used in order to transmit the multipleframes across the channel 19 with minimal interference. Each frameoutput from the respective OFDM modulator 16 is transmitted by arespective antenna 18. The antennas 18 may be spaced apart from eachother by any desirable separation. For example, the separation distancemay be in a range from a few millimeters to several meters.

[0051]FIG. 4 illustrates an example of a frame 52 that is transmittedacross the channel 19 from the transmitting antennas 18 to the receivingantennas 20. The frame 52 comprises a preamble 54 comprising a number oftraining symbols N_(I) and cyclic prefixes G. The preamble 54 isinserted by the pilot/training symbol inserter 32 as mentioned above. Inaddition, the data frame 52 comprises a data portion 56 consisting of aplurality of OFDM data symbols N and cyclic prefixes G, which areinserted before each of the OFDM data symbols N. As previouslymentioned, the pilot/training symbol inserter 32 further inserts pilotsymbols (not shown) intermittently within the OFDM data symbols N. Thetask of the preamble 54 and training symbols N_(I) in the frame is tohelp the receiver 10 identify the arrival of the frame 52 and henceperform time synchronization, frequency synchronization, and channelparameter estimation.

[0052] The preamble 54, in general, consists of Q or more trainingsymbols, wherein each training symbol has a length of G+N_(I) samples intime. The number of samples N_(I) is established as a certain fractionof the number of data samples N in an OFDM block such that N_(I)=N/I,where I is an integer, such as 1, 2, 4 . . . . For example, N_(I) may be¼ N. If no predetermined N_(I) has been established, the variable N_(I)may be given the value equal to N. The training symbol length may beshorter than the length of the symbols in the data portion 56, which hasa length of G+N samples.

[0053]FIG. 5 shows a portion of the MIMO OFDM communication system 6 ofFIG. 1 along with details of a signal transmission matrix S and areceived demodulated OFDM sample matrix R. The signals of thecommunication system 6 can be expressed using the equation:

R _(k,T×L) =S _(k,T×Q)η_(k,Q×L) +W _(k,T×L)

[0054] where R is a T×L received demodulated OFDM sample matrix, η is aQ×L matrix of channel coefficients that are indicative of thecharacteristics of the channel across which the signals are transmitted,S is a T×Q signal transmission matrix, and W is a T×L noise matrix thatcorrupts and distorts the received sample matrix R. In general, T may ormay not be equal to Q. However, for simplicity, T is assumed to be equalto Q herein.

[0055] The signal transmission matrix S shown in FIG. 5 consists of QOFDM symbols that are simultaneously transmitted from Q transmitantennas 18 over Q or more OFDM symbol periods (T_(s)). For example, ata first time instance t, the OFDM symbols S₁, S₂, . . . S_(Q) aretransmitted from the first to the Qth antennas 18. At a second timeinstance t+T_(S), the OFDM symbols S_(Q+1), S_(Q+2), . . . S_(2Q) aretransmitted from the same antennas 18. The OFDM symbol transmissions arerepeated at each time instance until all of the OFDM symbols of thematrix S have been transmitted.

[0056] During the transmission of training symbols in an initialcalibration mode, the S matrix consists of Q or more training symbols,each of which is less than or equal to the length of an OFDM symbol inthe time dimension. The training symbols are simultaneously transmittedfrom the transmitting antennas 18 as represented by equations (1) and(2), wherein the different antennas correspond to the space dimension.

[0057] During the transmission of the data symbols, after thecommunication system 6 has been calibrated, the S matrix consists of Qor more data symbols each occupying an OFDM symbol in the timedimension. The pilot/training symbol inserter 32 inserts the pilotsymbols within the data symbols. The data symbols are encoded,modulated, and transmitted from the transmitting antennas 18.

[0058] Each signal transmission matrix S of Q×Q OFDM symbols aretransmitted over the communication channel 19, which naturally comprisesa matrix of channel coefficients η. Typically, the communication channel19 further includes characteristics that distort and degrade thetransmitted signal, thereby adding a noise matrix W, before the signaltransmission matrix S is received at the L receive antennas 20.

[0059]FIG. 5 further illustrates how each of the L receiving antennas 20receives each of the Q transmitted signals. For example, the firstreceive antenna 20 receives OFDM signals over channel impulse responsesh₁₁, h₂₁, h₃₁ . . . h_(Q1) from the first to the Qth transmittingantennas 18, respectively. The term h_(i,j) refers to the channelimpulse response from the i^(th) transmit to the j^(th) receive antennain the time domain. The last receive antenna 20 receives the transmittedsignals over the channel impulse responses h_(1L), h_(2L), h_(3L), . . .h_(QL) from the first to the Qth transmitting antennas 18, respectively.For simplicity, only the signals received at the first and lastreceiving antennas 20 are shown. However, it should be understood thateach receiving antenna 20 receives the signals transmitted from the Qtransmitting antennas 18.

[0060] The received signals are demodulated by the respective OFDMdemodulators 22, which provide the received demodulated OFDM samplematrix R. At a time instance t, the samples R₁, R_(Q+1), . . .R_((L−1)Q+1) are received. At a next time instance t+Ts, the samples R₂,R_(Q+2) . . . R_((L−1)Q+2) are received. The samples are received ateach time instance until all of the samples in the received demodulatedOFDM sample matrix R are received. It should be noted that the timeinstances used for the matrices S and R are given the same variable,but, in essence, a delay occurs as is well known in the art.

[0061] A significant task of the receiver 10 is to estimate the time ofarrival of the transmitted signal. This process is called “timesynchronization.” In addition to time synchronization, OFDM systemstypically require frequency synchronization as well. Because thereusually exists a certain difference between the local oscillatorfrequencies of the transmitter and the receiver, the received signalsexperience a loss of sub-carrier orthogonality, which should typicallybe corrected in order to avoid degradation in system performance.

[0062]FIG. 6 shows a detailed illustration of the received demodulatedOFDM sample matrix R which consists of L columns and Q or more rows ofOFDM symbols with respect to space and time, respectively. As shown, thematrix R consists of three dimensions, namely space, time and frequency.The frequency axis indicates the amplitude of the frequency componentreceived at each receiving antenna 20 from each transmitting antenna 18.Each of the matrices R and η can be seen to consist of either N matricesof dimension Q×L or Q×L vectors of length N.

[0063] In general, the training symbol length may be equal to the datasymbol length. However, it is not necessary for the length of thetraining symbol in the preamble to be (N+G) since it is possible toestimate the characteristics of the channel even if the training symbollength is shortened to N_(I)+G such that (N₁+G)<(N+G). The variableN_(I) may be set so as to establish a range of frequencies that may beestimated. For example, if N_(I)=N/4, then a frequency offset of 4sub-carrier spacings can be estimated using the training symbol.However, the range to be established may depend upon the characteristicsof the channel to be estimated.

[0064] Transmission of the training sequence of length N_(I) correspondsto exciting every Ith sub-channel of an OFDM signal having a block sizeN. This means that no information is transmitted on the remaining(1−1/I)N sub-channels and the estimates of the channel for thesub-channels are derived from the ones that actually includeinformation.

[0065] The sub-channels of the transmit sequence that bear noinformation are said to be zero-padded. Alternatively, the trainingsequence of length N_(I) may be generated by first modulating every Ithsub-channel of the OFDM block by a known sequence of symbols and zeropadding the rest. An N-point IDFT is taken to obtain N samples in thetime domain, and finally only the first N_(I) samples along with itscyclic prefix are transmitted. At the receiver after synchronization,the samples corresponding to the training sequence of length N_(I) arerepeated I times before being demodulated by the OFDM demodulators. In anumber of alternative systems, many more sub-channels are zero padded toreduce the interference between the adjacent bands and to facilitate thesystem implementation. For example, in the systems based on the IEEE802.16a/b standard, a total of 56 tones or sub-carriers are zero padded.

[0066] The training sequence structure in the frequency domain isrepresented by its signal transmission matrix, which is configured insuch a way so as to have certain properties that aid in synchronizationand channel estimation. For example, the signal transmission matrix fora 2×2 system may be of the form: $\begin{matrix}{{S_{k} = \begin{bmatrix}S_{1,k} & S_{1,k} \\{- S_{1,k}^{*}} & S_{1,k}^{*}\end{bmatrix}},} & (1)\end{matrix}$

[0067] where * denotes a complex conjugate operation, and k is asub-carrier or sub-channel index. The signal transmission matrix S for a4×4 system may be of the form: $\begin{matrix}{{S_{k} = \begin{bmatrix}S_{1,k} & S_{1,k} & S_{1,k} & S_{1,k} \\{- S_{1,k}} & S_{1,k} & {- S_{1,k}} & S_{1,k} \\{- S_{1,k}} & S_{1,k} & S_{1,k} & {- S_{1,k}} \\{- S_{1,k}} & {- S_{1,k}} & S_{1,k} & S_{1,k}\end{bmatrix}},} & (2)\end{matrix}$

[0068] where S₁ is the sequence in the frequency domain that has certainproperties that satisfy the system requirements. Similarly, the signaltransmission matrix S for a 3×3 system may be of the form:$\begin{matrix}{{S_{k} = \begin{bmatrix}S_{1,k} & S_{2,k} & \frac{S_{3,k}}{\sqrt{2}} \\{- S_{2,k}^{*}} & S_{1,k}^{*} & \frac{S_{3,k}}{\sqrt{2}} \\\frac{S_{3,k}^{*}}{\sqrt{2}} & \frac{S_{3,k}^{*}}{\sqrt{2}} & \frac{{- S_{1,k}} - S_{1,k}^{*} + S_{2,k} - S_{2,k}^{*}}{2} \\\frac{S_{3,k}^{*}}{\sqrt{2}} & \frac{- S_{3,k}^{*}}{\sqrt{2}} & \frac{S_{2,k} + S_{2,k}^{*} + S_{1,k} - S_{1,k}^{*}}{2}\end{bmatrix}},} & (3)\end{matrix}$

[0069] The rows of the signal transmission matrix represent the timedimension, the columns represent the space dimension and the index krepresents the frequency dimension or the corresponding sub-carrier. Thetransmitter 8 may create the matrix S_(k) such that it is unitary. Ifthe vectors of the training sequences are derived from the points alongthe unit circle in the complex domain then the signal transmissionmatrices S_(k) shown in (1) and (2) are unitary. Besides making each ofthe transmission matrices S_(k) unitary, it also facilitates the systemimplementation and maintains a low PAPR of the sequence structure in thetime domain. This is because the signal transmission matrices in thetraining mode and the data mode are exactly alike, which furthersimplifies the system implementation. The transmission of a unitarymatrix aids in parameter estimation, as is described below.

[0070] With reference again to FIG. 1, the L number of receivingantennas 20 receive the Q number of transmitted signals and provide thereceived signals to respective OFDM demodulators 22, which down-convertthe signal back to baseband. The L number of receiving antennas 20 areseparated by a distance such that the received signals have minimumcorrelation and are as independent from each other as possible. Theoutputs from the L number of OFDM demodulators 22 are input into adecoder 24, which combines the multiple signals and decodes them. Inaddition, the decoder 24 removes any correctable noise and distortionerrors, as will be described below, and outputs signals representativeof the original data.

[0071]FIG. 7 illustrates an example embodiment of one of the OFDMdemodulators 22 of the receiver 10. Received signals from the receivingantenna 20 are input into a pre-amplifier 57, which amplifies thereceived signals to a level at which further processing may beperformed. The output of the pre-amplifier 57 is connected to a mixer58. A local oscillator 59 provides a signal to the mixer 58 having afrequency designed to demodulate the received amplified signal. Thedemodulated signal is then output to an analog-to-digital converter(ADC) 60, which converts the analog signals into discrete time samples.The discrete time samples are applied to a synchronization circuit 61.

[0072] An explanation will now be made to emphasize the significance ofsynchronization in an OFDM system. OFDM typically requires substantialsynchronization in time as well as in frequency in order thattransmitted signals can be recovered with adequate accuracy. Timesynchronization involves determining the best possible time for thestart of the received frame to closely match the start of thetransmitted signal.

[0073] Frequency synchronization involves maintaining orthogonality ofthe respective sub-carrier frequencies. Orthogonality refers to acondition of the sub-carrier frequencies wherein the “inner product” ofthe signals at different sub-carrier frequencies is zero. With respectto the inner product, reference is made, for example, to the time domainsequences s_(1,n) wherein n=0, 1, . . . N−1 and the sub-carrier index kis equal to 1. When the sub-carrier index k is equal to 2, the timedomain sequences S_(2,n) are transmitted. The inner product is equal toΣ(S_(1,n))*(S_(2,n)) wherein n=0, 1, . . . N−1. When the inner productis not equal to zero, a loss of sub-channel orthogonality may result,thereby causing Inter Carrier Interference (ICI). Since the sub-channels20 are separated by a precise frequency difference to maintainorthogonality, any difference in frequencies between the transmitter andthe receiver local oscillators may cause a loss of sub-channelorthogonality. The synchronization circuit 61 corrects this loss ofsub-channel orthogonality by finding an estimate of the differencebetween the frequencies of the local oscillators 46 of the transmitter 8and the frequencies of the local oscillators 59 of the receiver 10. Thesynchronization circuit 61 further corrects these frequency differenceestimates.

[0074] The frequency and time synchronized information is provided tothe cyclic prefix remover 62, which removes the cyclic prefixes insertedbetween each block of N symbols. The blocks of N samples are thenserial-to-parallel converted using serial-to-parallel converter 63 andthe parallel signals are input to a Discrete Fourier Transform (DFT)stage 64, which converts the time domain samples back to the frequencydomain, thus completing synchronization and demodulation by the OFDMdemodulators 22.

[0075] Referring again to FIG. 1, the L number of demodulated signalsfrom each of the L number of OFDM demodulators 22 are then input intothe decoder 24, which processes the demodulated signals. The decoder 24may be configured in the manner shown in the example embodiment of FIG.8. The decoder 24 comprises a space-time processor 110 and a parameterestimator 112. Both the space-time processor 110 and parameter estimator112 receive the signals from each of the L number of OFDM demodulators22.

[0076] An output from the parameter estimator 112 is input into a symboldemapper 116 and a set of outputs is input into the space-time processor110. The output of the space-time processor 110 is converted fromparallel to serial by a parallel-to-serial converter 114 and then inputto the symbol demapper 116, which maps the symbols from thepredetermined alphabet back to the data bits. The output from the symboldemapper 116 is input into a channel decoder 118. The channel decoder118 decodes the data symbols by checking the parity that was added tothe symbols prior to transmission. Thus, the channel decoder 118 detectsand corrects errors in the data symbols and outputs the data in itsoriginal form. There can be an exchange of information between theparameter estimator 112, symbol demapper 116, and channel decoder 118 tocreate a feedback loop. If the channel decoder 118 detects too manyerrors in the training symbol such that correction of the errors is nolonger possible, then an “excessive-error” indication is made to theparameter estimator 112, which adjusts and corrects its estimates.

[0077] An explanation of the parameter estimator 112 will now be made.The parameter estimator 112 estimates characteristics of the channelacross which the communication signals are transmitted. These channelcharacteristics or parameters may include, for example, the channelcoefficient matrix η, signal to noise ratio (SNR), noise variance, timecorrelation, and frequency correlation. Having knowledge of the uniquechannel parameters enables the receiver 10 to compensate for the variousdistortions introduced by the channel. Since the receiver 10 does nothave prior knowledge of the channel parameters, the parameter estimator112 calculates an estimation of the channel parameters, based on thetraining sequence transmitted by the transmitter 8. However, because ofthe complex processing involved with the synchronization circuit 61 toprovide time and frequency synchronization and frequency offsetestimation, there is a need to design algorithms of the parameterestimator 112 having relatively low complexity to adhere to stringenttiming constraints. On the other hand, there is a need for thealgorithms to be highly effective for accurately estimating the channelparameters.

[0078] Before performing parameter estimation, the received OFDM samplesbelonging to the training symbols of length N_(I), and a copy of thetransmitted training sequences of length N_(I) are repeated I times andtheir N-point FFT are taken to convert the received OFDM sample matrix Rand the OFDM signal transmission matrix S to have their dimension in thefrequency domain to be N.

[0079]FIG. 9 shows a first embodiment of a method performed by theparameter estimator 112 for calculating channel estimates. The parameterestimator 112 derives its estimates using a Least Squares (LS) method.If a certain number T of training symbols are transmitted from all the Qtransmit antennas, where T≧Q, then the LS estimates are obtained as

{overscore (η)}_(k)=(S _(k) ^(H) S _(k))⁻¹ S _(k) ^(H) ·R _(k) k=0,1, .. . , N _(I)−1

[0080] where the signal transmission matrix S for each of thesub-carriers has T rows and Q columns. The channel estimates so obtainedminimize the error between the estimated channel coefficient matrix andthe actual channel coefficient matrix for each tone. It should be notedthat the N_(I) channel coefficient matrices so obtained are onlyrepresentative of those tones that were excited in the transmittedsequence. The channel coefficient matrix for all the other tones are setto zero.

[0081] Further, if LS method is used along with the use of the signaltransmission matrices such as the ones shown in (1) and (2), only Q OFDMsymbols of a generalized length N_(I) are needed to estimate the channelcoefficients accurately. In absence of any noise, the parameterestimator 112 provides the exact estimates of the channel coefficientmatrices. This type of parameter estimator is called a zero-forcingestimator. Also, by using the LS method, the hardware complexity isreduced considerably since the same circuitry can be used for the datasymbols as well as the training symbols.

[0082] Hence, if the transmitter 8 creates the transmitting matrices Ssuch that the matrices are unitary as described above, then the preamblemay be formed having only Q symbols. By using only Q symbols of anygeneralized length N_(I), useful bandwidth is preserved for thetransmission of data. Furthermore, the training symbols may be createdhaving fewer than Q symbols, and may even be created having one symbol.

[0083] The method of FIG. 9 comprises a first step (block 120) forcalculating coarse channel estimates of the channel coefficient matrixusing an LS method and/or a Zero forcing method, if the S_(k)s areunitary. The coarse channel estimates may be calculated by a formulasuch as:

{overscore (η)}_(k) =S _(k) ^(H) ·R _(k) =S _(k) ⁻¹ ·R _(k)=η_(k)+{overscore (W)} _(k) k=0,1, . . . , N _(I)−1

[0084] where W_(k)=(S_(k) ⁻¹)(W_(k)).

[0085] An aim of the channel estimation algorithm is to estimate thechannel coefficients for all the sub-carriers. However, when N_(I) isnot equal to N, the channel coefficients are available for only N_(I)sub-carriers since the received OFDM training symbol of length N_(I) wasrepeated I times before passing through the DFT stage 64. To estimatethe sub-carriers that were not excited, a frequency domain interpolationprocedure is performed, as indicated by block 124. When the channelcomprises a bandwidth that is substantially coherent, a simple linearinterpolation scheme may be used to estimate the unexcited sub-carriers.Of course, when I is high, more sub-carriers are estimated from lessinformation, which results in an increase in the mean square error (MSE)in the channel estimates. In the case of increased MSE, a more complexinterpolation scheme may be used. Therefore, there is a tradeoff betweenthe greater training sequence overhead to minimize the estimationerrors, allowing a simpler interpolation scheme to be used, and a lessertraining sequence overhead to avoid taking up useful bandwidth,requiring a more complex interpolation scheme.

[0086] Once frequency interpolation has been carried out, flow proceedsto a step of reducing the mean square error (block 126). Since the MSEof the channel estimates of the LS estimator is typically high, it isusually desirable to reduce the MSE. One way of reducing MSE involvesthree steps, wherein a first step includes multiplying the coarsechannel estimate with the inverse matrix F⁻¹, which is the inverse of aunitary Fourier transform matrix F given by:$F = {\frac{1}{\sqrt{N}}\begin{bmatrix}1 & 1 & \ldots & 1 \\1 & \omega & \ldots & \omega^{N - 1} \\1 & \omega^{2} & \ldots & \omega^{2 \cdot {({N - 1})}} \\\vdots & \quad & \quad & \vdots \\1 & \omega^{N - 1} & \ldots & \omega^{{({N - 1})}^{2}}\end{bmatrix}}$

[0087] where ω=e^(2πj/N). This multiplication operation may be expressedas: $\begin{matrix}{{\underset{\_}{g}}_{ij} = \quad {{{IFFT}_{N}\left\{ {\underset{\_}{\overset{\_}{n}}}_{ij} \right\}} = {F^{- 1}\left\{ {{\underset{\_}{\eta}}_{ij} + {\overset{\_}{\underset{\_}{W}}}_{ij}} \right\}}}} \\{= \quad \begin{Bmatrix}{{\sqrt{N}{H_{{ij},m}(0)}} + {\overset{\_}{\omega}}_{{ij},m}} & {0 \leq m \leq {G - 1}} \\{\overset{\_}{\omega}}_{{ij},m} & {G \leq m \leq {N - 1.}}\end{Bmatrix}}\end{matrix}$

[0088] where 1 is less than or equal to i which is less than or equal toQ and 1 is less than or equal to j which is less than or equal to L. TheQ*L number of length-N vectors are then made to go through a windowingoperation, which reduces the effect of noise variations in the channeland ICI. The windowing operation recovers the time domain fine channelestimates whereby: ${\hat{h}}_{{ij},m} = \left\{ \begin{matrix}g_{{ij},m} & {0 \leq m \leq \left( {G - 1} \right)} \\0 & {m \geq G}\end{matrix} \right.$

[0089] The time domain fine channel estimates are then converted tofrequency domain fine channel estimates by performing a Fast FourierTransform (FFT) on the time domain fine channel estimates wherein:$\begin{matrix}{{{\underset{\_}{\hat{\eta}}}_{ij} = \quad {{{FFT}_{N}\left\{ {\hat{\underset{\_}{h}}}_{ij} \right\} \quad 1} \leq i \leq Q}},{1 \leq j \leq L}} \\{= \quad {{\underset{\_}{\eta}}_{ij} + {\hat{\underset{\_}{W}}}_{ij}}}\end{matrix}$

[0090] The reduction of the MSE by the previous steps provides accurateestimates of the channel parameters. The parameter estimator 112 outputsthese fine channel estimates to the space-time processor 110 in order tocompensate for the unique channel parameters. Other than the methodmentioned above to reduce the MSE in LS channel estimates, one canfollow a number of different methods for reducing MSE, such asperforming a smoothing procedure in the frequency domain, a LinearPrediction (LP) procedure, a method where decisions are fed back to theparameter estimator, a decision directed update procedure, etc.

[0091] Alternative to the embodiment of FIG. 9, another embodiment forcalculating fine channel estimates is illustrated in FIG. 10. Thisembodiment refers to the case mentioned earlier when a large number ofsub-carriers of the OFDM symbols are zero padded to facilitate thesystem implementation. Again, it is assumed that the signal transmissionmatrix S may or may not be unitary and the training sequence includes Qtraining symbols. In this alternative embodiment, a first step (block130) includes zero padding the recommended sub-carriers in the trainingsequence. For example, in a system that meets the IEEE 802.16a/bstandard, sub-carriers at DC and 55 others at high frequencies are setto zero. This sub-carrier elimination step simplifies the systemimplementation and further avoids adjacent channel interference. Forsuch systems, the above-mentioned method of MSE reduction typicallycannot be applied directly since the channel estimates are not availablefor the zero-padded sub-carriers. For such systems, the sub-carrierfrequencies that are eliminated may be initially estimated usingfrequency domain extrapolation on the existing LS estimates. The channelestimates at DC can be calculated by taking an average of the channelestimates for the positive and negative frequencies. The estimates ofthe eliminated sub-carrier frequencies are used to reduce the MSE in thechannel estimates but are otherwise ignored since no data is transmittedon these sub-carriers. Once channel estimates for all the sub-carriersare available, the procedure for MSE reduction mentioned above, alongwith a procedure for noise variance estimation that is described below,can be implemented. In a later step, the fine channel estimates for thezero-padded sub-carriers may be discarded.

[0092] After the zero-padding of tones, represented by step 130 in FIG.10, the coarse channel estimates are calculated using the LS methodand/or Zero forcing estimation, as indicated by block 134. This step isidentical to the corresponding step 120 of FIG. 9. Block 136 includesthe step of performing interpolating in the frequency domain. This stepis identical to step 124 of FIG. 9. When frequency domain interpolationhas been completed, the method of FIG. 10 proceeds to block 138 where afrequency domain extrapolation takes place. Frequencies at the low andhigh ends are extrapolated based on the existing middle frequencies inorder to arrive at the estimates for these frequencies.

[0093] At this point, a reduction in the MSE is calculated, as indicatedin block 140. All of the frequencies in the bandwidth, including theeliminated frequencies are used in the reduction of the MSE. Otherwise,step 140 is the same as step 126 of FIG. 9. Since only the MSE-reducedestimates of a selected range of transmitted sub-carrier frequencies areused, the unused frequencies outside the transmission bandwidth arediscarded, as indicated in block 142. Thus, the fine estimates of thechannel parameters having been obtained at the end of block 142, theparameter estimator 112 outputs the fine channel estimates to thespace-time processor 110.

[0094] In addition to the estimation of channel parameters, theparameter estimator 112 may further calculate estimates of the “noisevariance,” which is a parameter representing the power of the extraneousunwanted noise present in the signal. The noise variance is based on thecoarse channel estimates and can be obtained for each of the receivingantennas 20.

[0095]FIG. 11 shows an embodiment of a noise variance estimation method.Block 150 indicates a step wherein noise variance estimates for each ofthe receive antennas can be calculated. Assuming that the signaltransmission matrices S are unitary, the noise variance estimates can beobtained directly from the coarse channel estimates using the followingformula:$\sigma_{j}^{2} = {\frac{1}{Q}\left\lbrack {\frac{1}{2\left( {N - G} \right)}{\sum\limits_{i = 1}^{Q}{\sum\limits_{n = G}^{N - 1}{g_{{ij},n}}^{2}}}} \right\rbrack}$

[0096] where g_(ij,n) is the coefficient of the time domain coarsechannel estimates for the i^(th) transmit and j^(th) receive antennasobtained from the parameter estimator 112. The noise variance iscalculated for each of the j receive antennas.

[0097] In case the signal transmission matrix is not unitary, then thenoise variance estimation is a little more complex. This alternativenoise variance estimation method is shown in FIG. 12. A noise term isdetermined by subtracting the matrices of the fine channel estimatesfrom the matrices of the coarse channel estimates, whereby the coarsechannel estimates and fine channel estimates are obtained from theparameter estimator 112 as described above with reference to the channelestimation methods of FIGS. 9 and 10. Calculation of the noise terms isshown using block 152 and can be represented using the followingequation:

G′ _(k)={overscore (η)}_(k)−{circumflex over (η)}_(k) , k=0,1, . . . ,N−1.

[0098] The next step includes multiplying the matrices G_(k)′ with thesignal transmission matrices S_(k) as is represented by block 154 and asis given by the following equation:

G _(k) =S _(k) G′ _(k) , k=0,1, . . . , N−1.

[0099] The next step is to convert the frequency domain coefficients tothe time domain, which is represented using block 156 and can bedescribed using the following equation.

w _(ij) =IDFT{G _(ij)} 1≦i≦Q, 1≦j≦L

[0100] Finally, the estimates of the noise variance at each of thereceive antennas can be calculated using block 158 and is describedusing the following equation:$\sigma_{j}^{2} = {\frac{1}{Q}\left\lbrack {\frac{1}{2\left( {N - G} \right)}{\sum\limits_{i = 1}^{Q}{\sum\limits_{n = G}^{N - 1}{w_{{ij},n}}^{2}}}} \right\rbrack}$

[0101] The parameter estimator 112 may include software stored inmemory, wherein the software may include one or more separate programs,each of which comprises an ordered listing of executable instructionsfor implementing logical functions. In the example of FIGS. 9-12, thesoftware may include the parameter estimator system in accordance withthe present invention and a suitable operating system (O/S). Examples ofsuitable commercially available operating systems are as follows: aWindows operating system available from Microsoft Corporation; a Netwareoperating system available from Novell, Inc.; a Macintosh operatingsystem available from Apple Computer, Inc.; a UNIX operating systemavailable from the Hewlett-Packard Company, Sun Microsystems, Inc., orAT&T Corporation; a LINUX freeware operating system that is readilyavailable on the Internet; a run time Vxworks operating system availablefrom WindRiver Systems, Inc.; an appliance-based operating system, suchas that implemented in a handheld computer or personal data assistant(PDA) (e.g., PalmOS available from Palm Computing, Inc. or Windows CEavailable from Microsoft Corporation). The O/S essentially controls theexecution of other computer programs, such as the parameter estimationsystem, and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices.

[0102] The parameter estimator 112 can be implemented in hardware,software, firmware, or a combination thereof. In the embodiments of thepresent invention, the parameter estimator 112 can be implemented insoftware or firmware that is stored in a memory and that is executed bya suitable instruction execution system. If implemented in hardware, asin an alternative embodiment, the synchronization system can beimplemented with any or a combination of the following technologies,which are all well known in the art: a discrete logic circuit havinglogic gates for implementing logic functions upon data signals, anapplication specific integrated circuit (ASIC) having appropriatecombinational logic gates, a programmable gate array (PGA), a fieldprogrammable gate array (FPGA), digital signal processor (DSP), etc.

[0103] It should be emphasized that the above-described embodiments ofthe present invention are merely possible examples of implementations,merely set forth for a clear understanding of the principles of theinvention. Many variations and modifications may be made to theabove-described embodiments of the invention without departingsubstantially from the principles of the invention. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure and protected by the following claims.

We claim:
 1. An apparatus for estimating channel parameters in aMulti-Input, Multi-Output (MIMO) Orthogonal Frequency DivisionMultiplexing (OFDM) system, the apparatus comprising: a number Q of OFDMmodulators, each OFDM modulator producing a frame comprising at leastone inserted symbol, a plurality of data symbols, and cyclic prefixes;said number Q of transmitting antennas, each transmitting antennaconnected to a respective OFDM modulator, for transmitting said frameover a channel; a number L of receiving antennas for receiving thetransmitted frames; said number L of OFDM demodulators, each OFDMdemodulator corresponding to a respective receiving antenna; and an OFDMdecoder, connected to the output of each of the L OFDM demodulators, theOFDM decoder comprising a parameter estimator which processes thereceived frame in order to estimate the parameters of the channel. 2.The apparatus of claim 1, wherein the cyclic prefixes protect the datasymbols against Inter Symbol Interference (ISI).
 3. The apparatus ofclaim 1, wherein the at least one inserted symbol has at least one pilotsymbol inserted within the data symbols or at least one training symbolinserted at the beginning of the frame.
 4. The apparatus of claim 1,wherein the parameter estimator comprises a circuit for calculatingchannel estimates.
 5. The apparatus of claim 4, wherein the parameterestimator comprises a circuit for calculating coarse channel estimatesusing at least one of a least squares method and a zero-forcing method.6. The apparatus of claim 5, wherein the parameter estimator furthercomprises a circuit for interpolating values in the frequency domain. 7.The apparatus of claim 6, wherein the parameter estimator furthercomprises a circuit for reducing the mean square error of the coarsechannel estimates.
 8. The apparatus of claim 7, wherein the parameterestimator further comprises a circuit for zero-padding unused tones. 9.The apparatus of claim 8, wherein the parameter estimator furthercomprises a circuit for extrapolating values in the frequency domain.10. The apparatus of claim 9, wherein the parameter estimator furthercomprises a circuit for discarding unused tones.
 11. The apparatus ofclaim 1, wherein Q is equal to L.
 12. The apparatus of claim 11, whereinQ is equal to one.
 13. The apparatus of claim 1, wherein Q equals two.14. The apparatus of claim 1, wherein Q is not equal to L.
 15. Theapparatus of claim 1, wherein the OFDM encoder comprises: a channelencoder; a symbol mapper connected to an output of the channel encoder;a space-time processor connected to an output of the symbol mapper, thespace-time processor separating data into a plurality of sub-channels;and a pilot/training symbol inserter, which inserts pilot symbols andtraining symbols onto the sub-channels.
 16. The apparatus of claim 1,wherein each of the Q OFDM modulators comprises: a serial-to-parallelconverter; an inverse discrete Fourier transform (IDFT) stage connectedto an output of the serial-to-parallel converter; a cyclic prefixinserter connected to an output of the IDFT stage; a parallel-to-serialconverter connected to an output of the cyclic prefix inserter; adigital-to-analog converter (DAC) connected to an output of theparallel-to-serial converter; a local oscillator; a mixer having a firstinput and a second input, the first input connected to an output of theDAC, the second input connected to an output of the local oscillator;and an amplifier connected to an output of the mixer.
 17. The apparatusof claim 1, wherein the OFDM demodulator comprises: a pre-amplifier; alocal oscillator; a mixer having a first input and a second input, thefirst input connected to an output of the pre-amplifier, the secondinput connected to an output of the local oscillator; ananalog-to-digital converter (ADC) connected to an output of the mixer; asynchronization circuit having one input connected to an output of theADC; a cyclic prefix remover connected to an output of thesynchronization circuit; a serial-to-parallel converter connected to anoutput of the cyclic prefix remover; a discrete Fourier transform (DFT)stage connected to an output of the serial-to-parallel converter, anoutput of the DFT stage connected to another input to thesynchronization circuit.
 18. The apparatus of claim 1, wherein the OFDMdecoder comprise: a space-time processor that has a first set of inputs,a second set of inputs, and a first set of outputs, wherein the firstset of inputs receives samples from each of the L OFDM demodulators andthe second set of inputs receives estimates of channel parameters fromthe parameter estimator; the parameter estimator further comprising afirst set of inputs, a second input, a first set of outputs, and asecond output, wherein the first set of inputs receives samples fromeach of the L OFDM demodulators, and the second input receives feedbackfrom a channel decoder; a parallel-to-serial converter connected to thefirst set of outputs from the space-time processor; a symbol demapperhaving a first input a second input, a third input and a first outputwherein the first input is connected to an output of theparallel-to-serial converter, the second input connected to the secondoutput of parameter estimator, and the third input connected to afeedback from the channel decoder; and the channel decoder, connected toan output of the symbol demapper.
 19. A method for estimating parametersof a channel across which signals in a Multi-Input Multi-Output (MIMO)Orthogonal Frequency Division Multiplexing (OFDM) system aretransmitted, the method comprising the steps of: producing a frame ofdata comprising a preamble that includes a calibration component whichaids in parameter estimation, a plurality of data symbols, and aplurality of cyclic prefixes; transmitting the frame over a channel;receiving the transmitted frame; demodulating the received frame; andcalculating the estimates of the channel parameters from the receiveddemodulated frame.
 20. The method of claim 19, wherein the step ofproducing comprises producing a preamble that aids in synchronizationand parameter estimation.
 21. The method of claim 19, wherein the stepof producing comprises producing a preamble of a generalized lengthhaving a number of OFDM symbols less than a number of transmittingantennas.
 22. The method of claim 19, wherein the step of producingcomprises producing a preamble of a generalized length having a numberof OFDM symbols equal to a number of transmitting antennas.
 23. Themethod of claim 19, wherein the step of producing comprises producing apreamble of a generalized length having a number of OFDM symbols greaterthan a number of transmitting antennas.
 24. The method of claim 19,wherein the step of producing comprises producing a preamble whosesignal transmission matrix resembles an existing space-time block code.25. The method of claim 19, wherein the step of producing comprisesproducing cyclic prefixes in the preamble and in the data symbols suchthat the cyclic prefixes in the preamble are longer than the cyclicprefixes in the data symbols, thereby countering the extended channelimpulse response and improving the synchronization performance.
 26. Themethod of claim 19, wherein the step of calculating comprisescalculating fine channel estimates.
 27. The method of claim 26, whereinthe step of calculating fine channel estimates comprises: calculatingcoarse channel estimates using a least squares method or a zero-forcingmethod; interpolating the channel estimates in the frequency domain;reducing the mean square error of the coarse channel estimates.
 28. Themethod of claim 27, wherein the step of reducing the mean square errorcomprises: performing an IDFT operation on the coarse channel estimatesto convert the coarse channel estimates to the time domain; performing awindowing operation; and performing a Fast Fourier Transform (FFT)operation.
 29. The method of claim 27, wherein the step of reducing themean squared error comprises at least one of a linear predictionprocedure, a procedure for averaging the coarse channel estimates in thefrequency domain, a procedure for averaging the coarse channel estimatesin the time domain, a procedure for utilizing a decision feedbackequalizer, a decision directed procedure, a procedure utilizing afeedback from the channel decoder, a procedure for filtering the coarsechannel estimates in the frequency domain, and a procedure forprocessing the coarse channel estimates in the time or frequency domain.30. The method of claim 26, wherein the step of calculating the finechannel estimates comprise: zero-padding in the frequency domain toeliminate certain frequency components from the OFDM signal; calculatingcoarse channel estimates using a least squares method; employing azero-forcing method to calculate the coarse channel estimates;interpolating the channel estimates in the frequency domain;extrapolating frequency domain values; reducing the mean square error ofthe coarse channel estimates; and discarding the unused tones.
 31. Themethod of claim 30, wherein the step of reducing the mean squared errorcomprises at least one of a linear prediction procedure, a procedure foraveraging the coarse channel estimates in the frequency domain, aprocedure for averaging the coarse channel estimates in the time domain,a procedure for utilizing a decision feedback equalizer, a decisiondirected procedure, a procedure utilizing a feedback from the channeldecoder, a procedure for filtering the coarse channel estimates in thefrequency domain, and a procedure for processing the coarse channelestimates in the time or frequency domain.
 32. The method of claim 19,wherein the step of calculating comprises calculating a noise variance.33. The method of claim 32, wherein the step of calculating noisevariance estimates comprises calculating noise variance estimates for aunitary signal transmission matrix.
 34. The method of claim 33, whereinthe step of calculating noise variance estimates comprises calculatingthe noise variance estimates directly from time domain coefficients ofcoarse channel estimates.
 35. The method of claim 32, wherein the stepof calculating the noise variance estimates comprise calculating noisevariance estimates for a non-unitary signal transmission matrix.
 36. Themethod of claim 35, wherein the step of calculating the noise varianceestimates comprises the steps of: calculating a noise term from coarsechannel estimates and fine channel estimates; multiplying the noise termby the signal transmission matrix; calculating time domain noiseestimate vectors; and determining a noise variance estimation from thetime domain noise estimate vectors.
 37. The method of claim 32, whereinthe noise variance represents the power of the extraneous unwanted noisepresent in the signals.
 38. A method for estimating channel parametersin a Multi-Input, Multi-Output (MIMO) system, the method comprising:creating a preamble containing characteristics for calibrating the MIMOsystem; forming a signal transmission matrix that comprises thepreamble; solving a system of linear equations; and calculating a coarsechannel estimation from the result of the step of solving the system oflinear equations.
 39. The method of claim 38, wherein the step ofsolving the system of linear equations comprises the step of performinga least squares method.
 40. The method of claim 38, wherein the step ofsolving the system of linear equations comprises the step of performinga zero-forcing method.